Remove all records that have duplicates based on more than one variables

Question

I have data like this

df <- data.frame(var1 = c("A", "A", "B", "B", "C", "D", "E"), var2 = c(1, 2, 3, 4, 5, 5, 6 ))
#   var1 var2
# 1    A    1
# 2    A    2
# 3    B    3
# 4    B    4
# 5    C    5
# 6    D    5
# 7    E    6

A is mapped to 1, 2

B is mapped to 3, 4

C and D are both mapped to 5 (and vice versa: 5 is mapped to C and D)

E is uniquely mapped to 6 and 6 is uniquely mapped to E

I would like filter the dataset so that only

   var1 var2
7    E    6

is returned. base or tidyverse solution are welcomed.

I have tried

unique(df$var1, df$var2)
df[!duplicated(df),]
df %>% distinct(var1, var2)

but without the wanted result.

Answer 1

Using igraph::components.

Represent data as graph and get connected components:

library(igraph)
g = graph_from_data_frame(df)
cmp = components(g)

Grab components where cluster size (csize) is 2. Output vertices as a two-column character matrix:

matrix(names(cmp$membership[cmp$membership %in% which(cmp$csize == 2)]),
       ncol = 2, dimnames = list(NULL, names(df))) # wrap in as.data.frame if desired
#      var1 var2
# [1,] "E"  "6"

Alternatively, use names of relevant vertices to index original data frame:

v = names(cmp$membership[cmp$membership %in% which(cmp$csize == 2)])
df[df$var1 %in% v[1:(length(v)/2)], ]
#   var1 var2
# 7    E    6

---

Visualize the connections:

plot(g)

Answer 2

Using a custom function to determine if the mapping is unique you could achieve your desired result like so:

df <- data.frame(
  var1 = c("A", "A", "B", "B", "C", "D", "E"),
  var2 = c(1, 2, 3, 4, 5, 5, 6)
)

is_unique <- function(x, y) ave(as.numeric(factor(x)), y, FUN = function(x) length(unique(x)) == 1)

df[is_unique(df$var2, df$var1) & is_unique(df$var1, df$var2), ]
#>   var1 var2
#> 7    E    6